1.0 Field of the Invention
The invention relates generally to production and inventory control of a manufacturing facility or network of facilities and, more specifically, to a system and method that facilitates and coordinates improved planning and execution of such facilities in a supply chain with a focus on an improved planning, production and inventory control that is robust and effective, even in the presence of uncertainty.
2.0 Related Art
In recent years, companies have begun to appreciate the severity of the risks facing an entire corporation by not addressing potential supply chain problems. Indeed, more than two thirds of the companies surveyed by Accenture in 2006 said it had experienced a supply chain disruption from which it took more than one week to recover. Furthermore, the study revealed that 73% of the executives surveyed had a major disruption in the past 5 years. Of those, 36% took more than one month to recover. One reason for this maybe that supply chains are (1) not designed with risk in mind and (2) are not robust enough to operate under conditions significantly different from those for which they are planned.
For many years, people have sought to develop processes that will generate optimal plans and schedules for managing production systems and their supply chains. The goal has been to reduce inventory, improve customer service, and to reduce cost by increasing the utilization of expensive equipment and labor. Unfortunately, these processes have not explicitly considered risk and have ignored key facts regarding the systems they try to control.
The first attempts in this area date back to the 1960's beginning with Material Requirements Planning (MRP) that provided material plans with no consideration of capacity. This evolved into Manufacturing Resources Planning (MRP II), which provided some capacity checking modules around the basic MRP functionality and, eventually, into the Enterprise Resources Planning (ERP) systems used today.
In the remainder of this application, any production planning system will be referred to as an ERP system whether it carries that moniker or not. These can include so-called “legacy” systems that may employ only basic functionality such as MRP. Likewise, any system that is used to directly control execution, whether it is part of an ERP system or not, will be referred to as a “Manufacturing Execution System” (MES).
Interestingly, virtually all ERP systems today (including the high end offerings of SAP and Oracle) provide MRP functions and continue to have the same basic MRP calculations as the core of their production planning offering. Not surprisingly, a 2006 survey showed that users gave low marks to these high end ERP/SCM systems for performance in distribution and manufacturing (averaging 2.5 and 2.6, respectively, out of 4.0). In a survey performed by Microsoft of mid-sized companies (median revenue of $21 million, average around $ 100 million) 27% of 229 companies found their ERP/SCM system to be “ineffective” and 46% found it to be only “somewhat effective.” Only 3% found the system used to be “very effective.”
There are two basic problems with these systems: (1) order sizing is done without consideration of capacity and (2) planning lead times are assumed to be attributes of the part (see the book, Hopp and Spearman, Factory Physics, Foundations of Manufacturing Management, McGraw-Hill, New York, 2000, Chapter 5 for a complete discussion). The first issue results in conservative (i.e., large) order sizes which increase inventory and reduce responsiveness. The second issue is similar. Because the planning lead time does not depend on current work-in-process (WIP) levels and because being late (resulting in poor customer service) is worse than being early (resulting in extra inventory), most systems employ pessimistic (i.e., long) lead times. The result, again, is more inventory and less responsiveness and is especially aggravated when the bill of material is deep.
Efforts to address these problems have gone on for many years but the basic problems remain today. Consequently, most companies do not run their plants using the output of their ERP/SCM systems but, instead, “massage” the output using ad hoc spreadsheets.
Obviously, with this kind of “work around” there exists an opportunity to sell more sophisticated software and for some time now there have been numerous offerings known variously as “Advanced Planning and Scheduling” or “Advanced Planning and Optimization.” These “APO” applications typically work between the Enterprise Resources Planning (ERP) system and the Manufacturing Execution System (MES). Although a typical ERP system contains many functions other functions, this application focuses on those concerned with supply chain management such as demand forecasting, customer order tracking, supplier management, inventory tracking, capacity planning, master production scheduling, material requirements planning, and the management of product data including bills of material and routings. The MES is where the plans from the ERP are realized within the manufacturing facility and typically includes functions such as work in process (WIP) tracking, shop order dispatching, product costing, and equipment tracking.
The APO is a more recent development that attempts to remedy some of the aforementioned problems found in ERP. However, APO's are in the form of some type of deterministic simulation of the process that assume the demand, inventory, work in process, run rates, setup times, etc. are all known which then seek to generate a schedule that is “optimal” under some specified criteria. FIG. 1 presents an exemplary supply chain that includes a fabrication operation whose output is used in an assembly operation as well as a distribution function. FIG. 2 presents a computational APO application operating with an ERP system and an MES. FIG. 3 illustrates the interrelations between these computational functions.
One of the earliest APO's to appear that was moderately successful is called “Factory Planner®” and has been offered by i2 Corporation under various names (e.g., Rhythm®) since 1988. Offerings by Oracle® and SAP® in relatively recent products are different only in style and, perhaps, in the level of integration with other data.
Moreover, there are at least three problems that prevent the use of deterministic simulation as being an effective supply chain planning and scheduling tool:
1. The supply chain and plant have inherent randomness that do not allow for the complete specification of a time for each shop order at each process center with given labor component. Such detailed schedules are often quickly out of date because of the intrinsic variability in the system. Moreover they do not manage risk which involves random events that may or may not happen. In the past, this has been addressed using ever more detailed models requiring ever more computer power. This misses the point. Variability and risk are facts of life and are the result of not only process variation (something that one attempts to control) but also unforeseen events and variability in demand (things that cannot be controlled). At any rate, the result is the same regardless of the variability source. Detailed scheduling can only provide a very short term solution and, in practice, the solution often becomes invalid between the time it is generated and the time that the schedule is distributed and reviewed as part of production planning meetings.
2. The detailed scheduling system must be re-run often because of the short term nature of the solution. This becomes cumbersome and time consuming. Moreover, without a method for determining whether a significant change has occurred, oftentimes the schedule is regenerated in response to random noise (e.g., a temporary lull in demand) which is then fed back into the system. Unfortunately, feeding back random noise results in an increase in the variability in the system being controlled. Because of these problems, many companies have turned off their Advanced Planning and Scheduling systems after spending a great deal of money to install them.
3. It is essentially impossible to find an optimal schedule. The problems addressed are mathematically characterized as “NP-hard,” which means that no algorithm exists that works in “polynomial” time to solve scheduling problems. The practical result is that for realistic problems faced in modern factories and in the supply chain, there is not enough time to find an optimal schedule regardless of the speed of the computer. Consequently, heuristics must be applied to generate a, hopefully, near optimal schedule. The effectiveness of these heuristics is typically unknown for a broad range of applications.
These problems often result in a great deal of computer power being used to create a detailed schedule for a single instance that will never happen (i.e., the random “sample path” will never be what is predicted a priori) and that becomes obsolete as soon as something unanticipated occurs.
The most advanced systems today offer two methods of planning for manufacturing supply chains: (1) “what-if” analysis using a deterministic simulation of the supply chain and (2) “optimization” of a set of “penalties” (again, using a deterministic simulation) associated with inventory, on-time delivery, setups, and wasted capacity. There are also some crude methods for setting safety stock levels.
In addition to the fundamental problems listed above there are at two practical problems with this approach: (1) what-if analysis is tedious and (2) optimizing a penalty function is not intuitive.
The tediousness of what-if analysis comes from all the detail that must be considered. FIG. 4 shows the output of a typical APO prior art application for the scheduled production on six machines in one factory along with projected inventory plot of one of the items produced, discussed more below. The planner can move shop orders in the schedule and drill down on other items to view inventory projections. While this level of integration is impressive, it is not particularly useful especially when there are hundreds of machines (not to mention labor) to consider along with thousands of individual items, each with their own demand.
Likewise, the use of “penalties” to determine an “optimal” schedule is not intuitive. What should the penalty be for carrying additional inventory? What is the cost of a late order? What is the savings generated by reducing the number of setups, particularly if there is no reduction in head count? What is the cost of having idle machines?
FIG. 1 is a block diagram of a typical prior art supply chain that includes a fabrication operation whose output is used in an assembly operation as well as a distribution function. The important concept here is that the entire supply chain is comprised of only two types of components: stocks and flows.
Referring to FIG. 1, a simplified supply chain 1 includes Fabrication 2, Assembly 3, and Distribution 4. A plurality of raw materials come from a supplier 10 and are maintained in a Stock 11 until released into the product Flow 12 that may be embodied by one or more production processes. Once completed the product is either shipped immediately 14 (if the due date is passed) or is kept in a Stock 13 until the ship date (for make-to-order items) or until a demand occurs (for make-to-stock items). In this typical scenario some parts are shipped to an Assembly operation 3 and others directly to a Distribution center 4. In this example, the Assembly process brings together a plurality of parts from suppliers and fabrication, performs an assembly operation as well as other processes, and then either maintains an inventory in a Stock or ships to a Distribution site. The Distribution center comprising a Stock 15 and a kitting and shipping process 16 whereby the parts are shipped to satisfy Market Demand 17.
FIG. 2 is a block diagram of a typical prior art approach to supply chain management using an ERP system 50, an MES 54, and an APO system 52. Data for the ERP system is maintained in a data base 51 and includes all product information such as routings and bills of material as well as information regarding equipment and labor capacity along with demand information. Using data from the ERP system, the planner generates a plan for the period 53 which may be a week or more. The plan is then used to generate shop orders in the ERP system which is then executed using the Manufacturing Execution System 54, 58 and put into production 55. The MES also tracks WIP, cost, dispatch (i.e., prioritize) shop orders, track defects, etc. There may be scanners and sensors 56 that automatically collect data for WIP moving through the factory. There may also be computer monitors on the shop floor to indicate the status of the system 57.
FIG. 3 is a flow chart of a typical prior art approach to supply chain management using an ERP system. Long-term planning including the generation of a long term forecast 66, the capacity resource planning 70, together determine an aggregate production plan 68. Short-term planning brings in make-to-order (MTO) and make-to-stock (MTS) demands 72 as these occur into a Master Production Schedule 74. The planner may use the APO 88 to check capacity and due date feasibility of the schedule. Once a Master Production Schedule 74 is determined, Material Requirements Planning (MRP) 76 is used to generate demand for low level components. MRP data regarding the products and the system 84 includes bills of material to “explode” requirements for components based on the end product demand, inventory status data to determine net demand, lot sizing rules to determine the size of the shop orders, and planned lead times to determine when to launch purchase orders and shop orders. Once all of the demand for all of the components has been generated, netted, and lot sized, a pool of purchase orders and shop orders is generated as planned orders 78. The planner using the APO 88 then releases the shop orders at 80 to ensure on-time delivery and minimum inventory. This is typically done no more frequently than once per week. The Manufacturer's Execution System MES 86 tracks the shop orders as they go through production and provides information to the APO. These are released according to their release date if there is no APO. Alternatively, when an APO is present, it is used to determine an “optimal” release date that attempts to balance the conflicting desires of maintaining high utilization of resources while keeping inventories and cycle times low.
FIG. 4 is a representation of a prior art exemplary graphical user interface (GUI) 90 for an APO providing schedule and inventory information. This exemplary GUI presets a schedule for six machines 92 and numerous parts. One part 94 is highlighted and a plot of projected inventory is presented in the lower section of the GUI 96.
Accordingly, there is a need for an improved supply chain planning and scheduling tool that avoids one or more of the above drawbacks and limitations of the prior art.